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A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
An item bank will not only include the text of each item, but also extensive information regarding test development and psychometric characteristics of the items. Examples of such information include: [1] Item author; Date written; Item status (e.g., new, pilot, active, retired) Angoff ratings; Correct answer; Item format; Classical test theory ...
If the correlation between separate administrations of the test is high (e.g. 0.7 or higher as in this Cronbach's alpha-internal consistency-table [6]), then it has good test–retest reliability. The repeatability coefficient is a precision measure which represents the value below which the absolute difference between two repeated test results ...
The simplest method is to adopt an odd-even split, in which the odd-numbered items form one half of the test and the even-numbered items form the other. This arrangement guarantees that each half will contain an equal number of items from the beginning, middle, and end of the original test. [7]
In the realm of psychological testing and questionnaires, an individual task or question is referred to as a test Item or item. [6] [7] These items serve as fundamental components within questionnaire and psychological tests, often tied to a specific latent psychological construct (see operationalization). Each item produces a value, typically ...
Item tree analysis (ITA) is a data analytical method which allows constructing a hierarchical structure on the items of a questionnaire or test from observed response patterns. Assume that we have a questionnaire with m items and that subjects can answer positive (1) or negative (0) to each of these items, i.e. the items are dichotomous .
Items are chosen so that they comply with the test specification which is drawn up through a thorough examination of the subject domain. Foxcroft, Paterson, le Roux & Herbst (2004, p. 49) [9] note that by using a panel of experts to review the test specifications and the selection of items the content validity of a test can be improved. The ...
In order to model the characteristics of the items (e.g., to pick the optimal item), all the items of the test must be pre-administered to a sizable sample and then analyzed. To achieve this, new items must be mixed into the operational items of an exam (the responses are recorded but do not contribute to the test-takers' scores), called "pilot ...